Giant volumes of Bank Cheque are widely used all over the world for the financial transactions, especially in developing countries like Nepal. Paper Cheque is still used for non-cash transaction even after the implementation of electronic transactions such as debit and credit card system. These Cheques are processed manually, which require extra manpower, time and cost. Our attempt is to automate this process so that this labor-intensive work can be computerized which is both time and cost effective. In this paper we purpose a mechanism of recognition of check fields like Payee Name, Amount, Date and Account number. We propose a two-stage model where first phase includes the extraction and segmentation of required object from the Cheque and second is the recognition of those extracted character. These extracted characters provide the required information about the cheque that is to be processed. To automate the processing of cheque without human intervention the extraction and recognition of this character is necessary. The first phase extraction and segmentation of required object includes all Image processing activities. Firstly, we pre-process the Cheque images. Preprocessing includes separating foreground and background; improve the interpretability or perception of information in images and to provide ‘better’ input for the other image processing technique. After that, the enhanced image was then applied for the segmentation, which was done by using Connected Component Labeling. The segmented letters and digits that were extracted were fed into the Neural Network that is developed by using Back-propagation and gradient descent Machine learning algorithm which has 99% of accuracy with 0.1 learning rate. Finally, the machine shows its predicted result that were recognized by the system which was 75% accurate.